搜索资源列表
KMEANS(matlab)
- Matlab环境下的k-means聚类算法,实现图像分割,很快阿!-K-means Clustering arithmetic based on Matlab platform.It s fast for Image-Division!
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- 一种高效的聚类算法给定要聚类的N的对象以及N*N的距离矩阵(或者是相似性矩阵), 层次式聚类方法的基本步骤(参看S.C. Johnson in 1967)如下:-An Efficient Algorithm for the cluster must be the object of N and N * N distance matrix (or similarity matrix), the hierarchical clustering method the basic steps (see
一种高效的聚类算法
- 一种高效的聚类算法.大家可以来看下好不,绝对好的算法,不要错过-An efficient algorithm for clustering. We can look at the following bronzes, absolute good algorithm, should not miss! !
K-Means聚类
- k-means聚类算法源码。kmeans是一种常用的分割算法,简单而又高效。-k-means clustering algorithm source code. Kmeans is a common segmentation algorithm is simple but efficient.
最小二乘曲线拟和
- 最小二乘曲线拟和,是偏最小二乘拟和聚类的关键步骤之一 -least squares curve fitting, partial least squares fitting Clustering one of the key steps
h2
- 最新纯JAVA数据库,支持集群,支持私有内存数据库,-latest pure Java database, clustering support, the private support memory database
DataClusteringand-PatternRecognition
- 用matlab编程、用于数据聚类和模式识别的源代码-using Matlab programming for data clustering pattern recognition and the source code
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- 源码是有关模式识别与图像处理的,以及人工智能中的模糊算法,聚类算法 。-source of the pattern recognition and image processing, artificial intelligence and fuzzy algorithm, clustering algorithms.
rhrth
- 源码是有关模式识别与图像处理的,以及人工智能中的模糊算法,聚类算法 。-source of the pattern recognition and image processing, artificial intelligence and fuzzy algorithm, clustering algorithms.
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- 源码是有关模式识别与图像处理的,以及人工智能中的模糊算法,聚类算法 。-source of the pattern recognition and image processing, artificial intelligence and fuzzy algorithm, clustering algorithms.
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- isodata聚类算法,对于图像处理中的聚类问题高效 -isodata clustering algorithm for image processing and efficient clustering problem
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- 这是一个关于数据挖掘方面的源代码,它是关于聚类算法的代码-This is a data mining on the source code, it is about the clustering algorithm code
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- clustering in matlab with power full source
accord-machinelearning-clustering-(k-means)
- Accord.NET环境下的Kmeans聚类实例。(Kmeans clustering example under the Accord.NET environment.)
BBPSO
- BBPSO clustering for data bases like iris, vowel, cancer, etc.
kmedia
- 利用所编的程序,完美的实现经典的K-means聚类算法分析。(Using the program compiled, the classic K-means clustering algorithm is realized perfectly.)
K-mean Clustering and RBF _V_1.0
- Radial Basis Function with K Mean Clustering using Pseudo inverse method
Self-weighted Multiview Clustering
- Self-weighted Multiview Clustering with Multiple Graphs
Clustering
- 1) 使用凝聚型层次聚类算法(即最小生成树算法)对所有数据点进行聚类,最后聚成3类。相异度定义方法可选择single linkage、complete linkage、average linkage或者average group linkage中任意一种。 2) 使用C-Means算法对所有数据点进行聚类。C=3。 任务2(必做): 使用高斯混合模型(GMM)聚类算法对所有数据点进行聚类。C=3。并请给出得到的混合模型参数(包括比例??、均值??和协方差Σ)。 任务3(全做): 1) 参考数据文
Subtractive-Clustering-Algorithm-master
- 能够实现减法聚类,通过减法聚类对数据进行分类,使用python编程(It can realize subtraction clustering and use Python Programming)